Marketers, don’t ignore the most important parts of bringing the promise of analytics to life.
Your CMO may be reading headlines and talking to their executive peers about the pressure to become data-driven. If you’re a CMO, perhaps you’ve adopted that mandate, secured a budget, and are jumping head-first into the quest.
Ok … so, where do you start?
I had the chance to sit down with MarketingProfs’ Kerry O’Shea Gorgone for a podcast on this very topic. Listen to our conversation here, “How to Become a Data-Driven Company (Without a Data Scientist)”
For those of you who are embarking on the journey to “be data-driven,” here’s the path I recommend taking.
Start with the right understanding.
What does it mean to be data-driven? Any brand can make this claim, but very few actually live up to the promise.
Becoming data-driven doesn’t happen magically just because you’ve deployed new marketing technology, or hired analysts, or started collecting massive amounts of customer data.
A brand is truly data-driven when it:
- Understands that focusing on improving customer experiences will result in improved business outcomes
- Has a data strategy in place that defines why, what, how and when data is collected, stored, analyzed and activated
- Actively uses the data it collects in order to test, learn and personalize customer experiences
Create a data strategy and a roadmap.
The road to data-driven nirvana is paved with wrong turns.
If you’re in charge of building a data-driven organization, the worst thing that can happen is to lose the trust (or interest!) of your stakeholders if you don’t deliver what they *thought* you should be delivering.
The best way to avoid that scenario is to take the time before you buy and implement technology to define your data strategy.
First, find out the “why” behind the request to “be data-driven.” What are the desired outcomes? What business objectives and executive perspectives are driving the mandate?
Becoming data-driven requires us to start with a clear purpose.
When I help organizations build out their analytics roadmaps, I include both a high level view of large initiatives going three years into the future, and detailed views of the deliveries on a quarterly level.
This helps to visualize the behind-the-scenes work that must be done (and done right), and align it to the corporate goals that matter to stakeholders. It also gives you a way to celebrate milestones on a regular basis, bringing those stakeholders along on the journey with you as the process unfolds.
Becoming data-driven requires us to start with a clear purpose that maps to business strategy, motivated by the goal of activating data to improve your customers’ experiences.
Build use-cases. Don’t skip this step!
When software is built, engineers rely on use-cases to define specific situations in which a product could be used. Analytics strategy is no different.
With the “why” clarified, the second thing I would do after hearing “we need to become a data-driven organization” would be to roll out the process of using use cases to define data analysis and activation initiatives.
Example: Recently I met with a client to discuss a specific analytics implementation request from the sales team. The sales people wanted to have some data from the website pulled and passed into their CRM.
When I asked the team how they were going to use the data (e.g. what problems it would solve or opportunities it would offer), no one in the meeting was able to answer the question.
There were two problems with getting a request without having use cases defined:
- If there was no understanding of the business value of the request (how the data would be used to help the sales people), then there was no way to measure the ROI of the time and effort required to deploy the solution.
- Without an understanding of how the salespeople would use the data, there was no opportunity for us to make a recommendation on the most optimal solution.
Use cases may sound daunting to create, but they really are not. At its most elemental a use case is simply a simple statement such as:
Ex: As a <sales person>, if I can <see what website pages a lead visited on our website in context of our other CRM data>, I will be able to <better understand what type of content they are interested in, to prepare more relevant follow up communication>. I anticipate that I will be able to <close more sales when I am better prepared with a lead’s browsing behavior before reaching out to them>.
At first glance, this seems like a strong business use case for implementing a technological solution to pass website data into the CRM. But how can the organization quantify the potential ROI on the analytics and technical resources that will be required to design, implement and validate the solution?
One way would be to compare the lead-to-close sale rate of CRM accounts that have browsing activity vs. those that do not. We’d need to establish a hypothesis; if our baseline rate is X%, how much do we expect that to improve with these changes?
And that, my friends, is how you can use a very simple use case methodology to clearly define your requests to make sure they 1) are customer focused, 2) result in a positive business outcome.
Use cases are the most overlooked, under-leveraged, and important part of a winning data strategy.
Most organizations simply don’t focus on use cases at all – and ultimately this becomes a major mistake! Without clear use cases, organizations spend time and resources to collect data, and end up not putting it to use. Case in point: According to the Harvard Business Review, cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions, and less than 1% of its unstructured data is analyzed or used at all. (!!!)
We can do better than that, but only if we change the way we approach data collection in the first place. Use cases help us map out how the data will be used, in order to justify the resources for implementing a solution to collect and maintain the data.
Use cases are also critical when it comes down to deciding which technology you need to invest in. They prevent marketers from wasting time (and money) by quelling that instinct to jump right into taking demos for Martech solutions, even going so far as to purchase a tool, without understanding post-launch strategy and usage.
Don’t get caught spending money on a tool and letting it sit for months, even years, without using it. Every dollar can be better invested in another way to improve the customer experience.
Build the right team.
You can’t be data-driven without the right team. But, I advise all CMOs to not only hire for the most common technical and analytics skills. Instead, hire data strategists who are also hyper focused on these three key success factors:
- Aligning data collection and activation with customer and business needs (use cases)
- Creating and owning data strategy documentation
- Building and managing a data roadmap
If you’re building out the team now, consider that a data strategist may be just one role in your organization, or a combination of a few people with hybrid skills. Regardless of who performs the activity, I believe data strategy is the most important skill for today’s marketing analytics teams.
Data strategy is the most important skill for marketing analytics teams.
Grab this opportunity!
“Becoming a data-driven organization” sounds great, until it comes time to manifest this charter into practice. Success begins with the right understanding of what “being data-driven” means, a data strategy and roadmap that visualizes the journey at multiple levels, clear use cases for both data and technology, and the right team.
If my team here at Qualified Digital can be of any help, let us know (email@example.com). Sometimes it’s best not to go down the path towards data-driven nirvana alone.
PS: Don’t miss my podcast conversation on this topic with MarketingProfs’ Kerry O’Shea Gorgone here, “How to Become a Data-Driven Company (Without a Data Scientist)”
Linda Schumacher, Senior Director of Analytics and Data Strategy, Qualified Digital
Linda combines her love for digital strategy with 15 years of hands-on analytics expertise in her role at Qualified Digital, a CX company. She wakes up every day curious about making data, technology, and business work in harmony, and is committed to helping companies close the gap between analysts, technical teams, and their business stakeholders.
She has delivered beautiful experiences for customers of brands you know and many you don’t. Linda is an Adobe Certified Expert and a Google Analytics Certified Consultant and lives in Oakland, CA with her family. Connect with her on LinkedIn at linkedin.com/in/lindaschumacher